Overview

Dataset statistics

Number of variables9
Number of observations485
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory34.2 KiB
Average record size in memory72.3 B

Variable types

DateTime1
Numeric8

Alerts

fear_and_greed_index is highly overall correlated with fear_and_greed_slope_21 and 4 other fieldsHigh correlation
fear_and_greed_slope_3 is highly overall correlated with fear_and_greed_slope_8High correlation
fear_and_greed_slope_8 is highly overall correlated with fear_and_greed_slope_3 and 1 other fieldsHigh correlation
fear_and_greed_slope_21 is highly overall correlated with fear_and_greed_index and 5 other fieldsHigh correlation
fear_and_greed_ma_3 is highly overall correlated with fear_and_greed_index and 4 other fieldsHigh correlation
fear_and_greed_ma_8 is highly overall correlated with fear_and_greed_index and 4 other fieldsHigh correlation
fear_and_greed_ma_21 is highly overall correlated with fear_and_greed_index and 4 other fieldsHigh correlation
sz_close is highly overall correlated with fear_and_greed_index and 4 other fieldsHigh correlation
trade_date has unique valuesUnique

Reproduction

Analysis started2022-12-11 11:34:16.141988
Analysis finished2022-12-11 11:34:25.589049
Duration9.45 seconds
Software versionpandas-profiling vv3.5.0
Download configurationconfig.json

Variables

trade_date
Date

Distinct485
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size3.9 KiB
Minimum2020-12-11 00:00:00
Maximum2022-12-09 00:00:00
2022-12-11T19:34:25.650105image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-11T19:34:25.759710image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

fear_and_greed_index
Real number (ℝ)

Distinct435
Distinct (%)89.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean49.712953
Minimum1.893
Maximum98.107
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size3.9 KiB
2022-12-11T19:34:25.884373image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum1.893
5-th percentile6.96298
Q124.7737
median47.6543
Q374.2387
95-th percentile91.34154
Maximum98.107
Range96.214
Interquartile range (IQR)49.465

Descriptive statistics

Standard deviation27.555524
Coefficient of variation (CV)0.55429264
Kurtosis-1.2580547
Mean49.712953
Median Absolute Deviation (MAD)24.6091
Skewness0.0030020432
Sum24110.782
Variance759.3069
MonotonicityNot monotonic
2022-12-11T19:34:26.000983image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
34.9794 3
 
0.6%
38.0247 3
 
0.6%
70.7819 2
 
0.4%
22.6337 2
 
0.4%
48.4774 2
 
0.4%
21.3992 2
 
0.4%
53.9918 2
 
0.4%
42.2222 2
 
0.4%
83.0453 2
 
0.4%
55.7202 2
 
0.4%
Other values (425) 463
95.5%
ValueCountFrequency (%)
1.893 1
0.2%
2.4691 1
0.2%
2.716 1
0.2%
2.7984 1
0.2%
2.8807 1
0.2%
3.0453 1
0.2%
3.2099 1
0.2%
3.3745 1
0.2%
3.7037 1
0.2%
4.0329 1
0.2%
ValueCountFrequency (%)
98.107 1
0.2%
97.1193 1
0.2%
96.8724 2
0.4%
95.8848 1
0.2%
95.8025 1
0.2%
95.7202 1
0.2%
95.6379 1
0.2%
95.3909 1
0.2%
94.8148 1
0.2%
94.6502 1
0.2%

fear_and_greed_slope_3
Real number (ℝ)

Distinct444
Distinct (%)91.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.031281237
Minimum-20.960233
Maximum19.6159
Zeros1
Zeros (%)0.2%
Negative250
Negative (%)51.5%
Memory size3.9 KiB
2022-12-11T19:34:26.116593image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum-20.960233
5-th percentile-8.3950667
Q1-2.5788667
median-0.21946667
Q32.9081
95-th percentile8.9711733
Maximum19.6159
Range40.576133
Interquartile range (IQR)5.4869667

Descriptive statistics

Standard deviation5.3782586
Coefficient of variation (CV)171.93241
Kurtosis2.1857273
Mean0.031281237
Median Absolute Deviation (MAD)2.7434667
Skewness-0.25949324
Sum15.1714
Variance28.925665
MonotonicityNot monotonic
2022-12-11T19:34:26.297765image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
-0.9328 6
 
1.2%
0.631 3
 
0.6%
1.1797 3
 
0.6%
-1.893 3
 
0.6%
-4.142666667 2
 
0.4%
2.386833333 2
 
0.4%
-0.4115333333 2
 
0.4%
3.182433333 2
 
0.4%
-8.395066667 2
 
0.4%
5.459533333 2
 
0.4%
Other values (434) 458
94.4%
ValueCountFrequency (%)
-20.96023333 1
0.2%
-20.30176667 1
0.2%
-19.17696667 1
0.2%
-18.43623333 1
0.2%
-17.61316667 1
0.2%
-15.9122 1
0.2%
-15.77503333 1
0.2%
-15.5281 1
0.2%
-15.39096667 1
0.2%
-12.94926667 1
0.2%
ValueCountFrequency (%)
19.6159 1
0.2%
16.98216667 1
0.2%
16.10426667 1
0.2%
15.7476 1
0.2%
15.00686667 1
0.2%
14.95196667 1
0.2%
12.7572 1
0.2%
12.6749 1
0.2%
12.64746667 1
0.2%
12.5926 1
0.2%

fear_and_greed_slope_8
Real number (ℝ)

Distinct452
Distinct (%)93.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.055088789
Minimum-8.1069875
Maximum8.467075
Zeros0
Zeros (%)0.0%
Negative241
Negative (%)49.7%
Memory size3.9 KiB
2022-12-11T19:34:26.413373image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum-8.1069875
5-th percentile-5.2510275
Q1-1.4917625
median0.0514375
Q31.656375
95-th percentile4.820995
Maximum8.467075
Range16.574063
Interquartile range (IQR)3.1481375

Descriptive statistics

Standard deviation2.8809177
Coefficient of variation (CV)52.295898
Kurtosis0.52238182
Mean0.055088789
Median Absolute Deviation (MAD)1.5535
Skewness-0.16393787
Sum26.718063
Variance8.2996868
MonotonicityNot monotonic
2022-12-11T19:34:26.515970image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.9773625 9
 
1.9%
-0.7716125 3
 
0.6%
0.236625 3
 
0.6%
-0.1543125 2
 
0.4%
0.56585 2
 
0.4%
-0.1646125 2
 
0.4%
1.08025 2
 
0.4%
3.8786 2
 
0.4%
4.1255125 2
 
0.4%
-0.7818875 2
 
0.4%
Other values (442) 456
94.0%
ValueCountFrequency (%)
-8.1069875 1
0.2%
-7.952675 1
0.2%
-7.89095 1
0.2%
-7.7572 1
0.2%
-7.685175 1
0.2%
-7.561725 1
0.2%
-7.438275 1
0.2%
-7.263375 1
0.2%
-7.0679 1
0.2%
-7.0576125 1
0.2%
ValueCountFrequency (%)
8.467075 1
0.2%
7.7880625 1
0.2%
7.438275 1
0.2%
7.3353875 1
0.2%
6.9238625 1
0.2%
6.790125 1
0.2%
6.697525 1
0.2%
6.440325 1
0.2%
6.1522625 1
0.2%
6.1111125 1
0.2%

fear_and_greed_slope_21
Real number (ℝ)

Distinct443
Distinct (%)91.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.070684104
Minimum-3.8056048
Maximum4.0917143
Zeros0
Zeros (%)0.0%
Negative229
Negative (%)47.2%
Memory size3.9 KiB
2022-12-11T19:34:26.632580image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum-3.8056048
5-th percentile-2.6556933
Q1-1.1992952
median0.14500952
Q31.3011952
95-th percentile2.7113448
Maximum4.0917143
Range7.897319
Interquartile range (IQR)2.5004905

Descriptive statistics

Standard deviation1.6174974
Coefficient of variation (CV)22.883468
Kurtosis-0.53984555
Mean0.070684104
Median Absolute Deviation (MAD)1.1679429
Skewness-0.01840516
Sum34.28179
Variance2.6162979
MonotonicityNot monotonic
2022-12-11T19:34:26.747523image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1.301195238 22
 
4.5%
-0.2312380952 2
 
0.4%
1.391338095 2
 
0.4%
-1.136585714 2
 
0.4%
1.324709524 2
 
0.4%
1.269838095 2
 
0.4%
0.6153238095 2
 
0.4%
-1.211052381 2
 
0.4%
0.6662761905 2
 
0.4%
0.2077238095 2
 
0.4%
Other values (433) 445
91.8%
ValueCountFrequency (%)
-3.805604762 1
0.2%
-3.53517619 1
0.2%
-3.51557619 1
0.2%
-3.48422381 1
0.2%
-3.468547619 1
0.2%
-3.413680952 1
0.2%
-3.38232381 1
0.2%
-3.2961 1
0.2%
-3.252990476 1
0.2%
-3.237309524 1
0.2%
ValueCountFrequency (%)
4.091714286 1
0.2%
3.919266667 1
0.2%
3.836957143 1
0.2%
3.699785714 1
0.2%
3.656671429 1
0.2%
3.629238095 1
0.2%
3.578285714 1
0.2%
3.539095238 1
0.2%
3.460709524 1
0.2%
3.425438095 1
0.2%

fear_and_greed_ma_3
Real number (ℝ)

Distinct473
Distinct (%)97.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean49.678051
Minimum2.9355333
Maximum96.3786
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size3.9 KiB
2022-12-11T19:34:26.869139image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum2.9355333
5-th percentile9.16872
Q126.886167
median48.3402
Q373.251033
95-th percentile91.127587
Maximum96.3786
Range93.443067
Interquartile range (IQR)46.364867

Descriptive statistics

Standard deviation26.73315
Coefficient of variation (CV)0.53812799
Kurtosis-1.2309552
Mean49.678051
Median Absolute Deviation (MAD)23.813467
Skewness0.027646563
Sum24093.855
Variance714.6613
MonotonicityNot monotonic
2022-12-11T19:34:26.981248image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
70.04116667 3
 
0.6%
74.0192 2
 
0.4%
86.8587 2
 
0.4%
14.75996667 2
 
0.4%
34.54046667 2
 
0.4%
29.8491 2
 
0.4%
90.8642 2
 
0.4%
45.78873333 2
 
0.4%
16.81756667 2
 
0.4%
73.11386667 2
 
0.4%
Other values (463) 464
95.7%
ValueCountFrequency (%)
2.935533333 1
0.2%
3.209866667 1
0.2%
3.264766667 1
0.2%
3.5117 1
0.2%
3.9506 1
0.2%
4.197566667 1
0.2%
4.883433333 1
0.2%
5.1029 1
0.2%
5.377233333 1
0.2%
5.596666667 1
0.2%
ValueCountFrequency (%)
96.3786 1
0.2%
96.2963 1
0.2%
96.18656667 1
0.2%
96.15913333 1
0.2%
96.04936667 1
0.2%
95.47323333 1
0.2%
94.34843333 1
0.2%
93.93693333 1
0.2%
93.82713333 1
0.2%
93.66253333 1
0.2%

fear_and_greed_ma_8
Real number (ℝ)

Distinct470
Distinct (%)96.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean49.588435
Minimum4.166675
Maximum94.927975
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size3.9 KiB
2022-12-11T19:34:27.095856image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum4.166675
5-th percentile10.841545
Q128.086425
median46.923862
Q372.037025
95-th percentile89.31894
Maximum94.927975
Range90.7613
Interquartile range (IQR)43.9506

Descriptive statistics

Standard deviation25.429812
Coefficient of variation (CV)0.51281739
Kurtosis-1.2316472
Mean49.588435
Median Absolute Deviation (MAD)23.3642
Skewness0.03905241
Sum24050.391
Variance646.67533
MonotonicityNot monotonic
2022-12-11T19:34:27.198453image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
71.33745 8
 
1.6%
74.115225 2
 
0.4%
55.7613125 2
 
0.4%
74.156375 2
 
0.4%
20.740725 2
 
0.4%
31.563775 2
 
0.4%
92.4176875 2
 
0.4%
41.707825 2
 
0.4%
63.67285 2
 
0.4%
13.8785875 1
 
0.2%
Other values (460) 460
94.8%
ValueCountFrequency (%)
4.166675 1
0.2%
4.557625 1
0.2%
4.7736875 1
0.2%
4.79425 1
0.2%
4.7942625 1
0.2%
4.907425 1
0.2%
5.5452875 1
0.2%
5.6996 1
0.2%
6.0185375 1
0.2%
6.3786125 1
0.2%
ValueCountFrequency (%)
94.927975 1
0.2%
94.506175 1
0.2%
94.3004125 1
0.2%
94.2284 1
0.2%
94.1358125 1
0.2%
94.084375 1
0.2%
94.0329125 1
0.2%
93.981475 1
0.2%
93.74485 1
0.2%
93.25105 1
0.2%

fear_and_greed_ma_21
Real number (ℝ)

Distinct464
Distinct (%)95.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean49.596676
Minimum10.060762
Maximum90.507548
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size3.9 KiB
2022-12-11T19:34:27.304600image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum10.060762
5-th percentile16.411511
Q129.265129
median48.943762
Q369.72369
95-th percentile84.72506
Maximum90.507548
Range80.446786
Interquartile range (IQR)40.458562

Descriptive statistics

Standard deviation22.752141
Coefficient of variation (CV)0.45874326
Kurtosis-1.2473229
Mean49.596676
Median Absolute Deviation (MAD)20.050952
Skewness0.036367143
Sum24054.388
Variance517.65991
MonotonicityNot monotonic
2022-12-11T19:34:27.414377image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
78.2990381 21
 
4.3%
71.36586667 2
 
0.4%
21.03860952 1
 
0.2%
20.62315238 1
 
0.2%
22.10855238 1
 
0.2%
23.16283333 1
 
0.2%
23.57827619 1
 
0.2%
23.3392 1
 
0.2%
22.67292857 1
 
0.2%
22.21829524 1
 
0.2%
Other values (454) 454
93.6%
ValueCountFrequency (%)
10.0607619 1
0.2%
10.12347143 1
0.2%
10.1626619 1
0.2%
10.55457143 1
0.2%
10.76229524 1
0.2%
10.89554762 1
0.2%
11.0562381 1
0.2%
11.16991429 1
0.2%
11.37761905 1
0.2%
11.38937619 1
0.2%
ValueCountFrequency (%)
90.50754762 1
0.2%
90.13521905 1
0.2%
89.64923333 1
0.2%
89.09661429 1
0.2%
88.72819524 1
0.2%
88.7203619 1
0.2%
88.72035714 1
0.2%
88.26572857 1
0.2%
88.19517619 1
0.2%
87.94826667 1
0.2%

sz_close
Real number (ℝ)

Distinct484
Distinct (%)99.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3391.5981
Minimum2886.43
Maximum3715.37
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size3.9 KiB
2022-12-11T19:34:27.531534image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum2886.43
5-th percentile3041.836
Q13243.45
median3446.98
Q33556.56
95-th percentile3629.786
Maximum3715.37
Range828.94
Interquartile range (IQR)313.11

Descriptive statistics

Standard deviation194.46277
Coefficient of variation (CV)0.05733662
Kurtosis-0.7824873
Mean3391.5981
Median Absolute Deviation (MAD)142.11
Skewness-0.56826478
Sum1644925.1
Variance37815.77
MonotonicityNot monotonic
2022-12-11T19:34:27.633132image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
3566.38 2
 
0.4%
3347.19 1
 
0.2%
3167.13 1
 
0.2%
2958.28 1
 
0.2%
2886.43 1
 
0.2%
2928.51 1
 
0.2%
3086.92 1
 
0.2%
3079.81 1
 
0.2%
3151.05 1
 
0.2%
3194.03 1
 
0.2%
Other values (474) 474
97.7%
ValueCountFrequency (%)
2886.43 1
0.2%
2893.48 1
0.2%
2915.93 1
0.2%
2928.51 1
0.2%
2958.28 1
0.2%
2969.2 1
0.2%
2974.15 1
0.2%
2975.48 1
0.2%
2976.28 1
0.2%
2977.56 1
0.2%
ValueCountFrequency (%)
3715.37 1
0.2%
3703.11 1
0.2%
3696.17 1
0.2%
3693.13 1
0.2%
3681.08 1
0.2%
3676.59 1
0.2%
3675.36 1
0.2%
3675.19 1
0.2%
3675.02 1
0.2%
3673.04 1
0.2%

Interactions

2022-12-11T19:34:24.656038image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-11T19:34:19.445165image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-11T19:34:20.200386image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-11T19:34:20.947442image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-11T19:34:21.666275image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-11T19:34:22.477545image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-11T19:34:23.164747image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-11T19:34:23.839265image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-11T19:34:24.735613image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-11T19:34:19.534754image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-11T19:34:20.290973image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-11T19:34:21.034023image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-11T19:34:21.755858image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-11T19:34:22.559620image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-11T19:34:23.244321image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-11T19:34:23.928849image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-11T19:34:24.826321image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-11T19:34:19.630347image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-11T19:34:20.389566image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-11T19:34:21.131268image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-11T19:34:21.856454image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-11T19:34:22.653210image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-11T19:34:23.337910image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-11T19:34:24.030446image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-11T19:34:24.908900image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-11T19:34:19.771922image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-11T19:34:20.485157image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-11T19:34:21.219852image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-11T19:34:21.952045image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-11T19:34:22.742795image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-11T19:34:23.423490image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-11T19:34:24.125035image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-11T19:34:25.000487image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-11T19:34:19.867513image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-11T19:34:20.585753image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-11T19:34:21.318444image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-11T19:34:22.118201image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-11T19:34:22.835382image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-11T19:34:23.517080image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-11T19:34:24.225631image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-11T19:34:25.080063image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-11T19:34:19.949650image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-11T19:34:20.675641image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-11T19:34:21.405029image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-11T19:34:22.205786image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-11T19:34:22.917516image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-11T19:34:23.596655image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-11T19:34:24.313214image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-11T19:34:25.159135image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-11T19:34:20.027223image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-11T19:34:20.762267image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-11T19:34:21.489609image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-11T19:34:22.293370image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-11T19:34:22.995089image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-11T19:34:23.673540image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-11T19:34:24.475867image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-11T19:34:25.252725image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-11T19:34:20.122814image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-11T19:34:20.862862image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-11T19:34:21.586201image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-11T19:34:22.394968image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-11T19:34:23.089678image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-11T19:34:23.764693image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-11T19:34:24.573460image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Correlations

2022-12-11T19:34:27.793831image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Auto

The auto setting is an interpretable pairwise column metric of the following mapping:
  • Variable_type-Variable_type : Method, Range
  • Categorical-Categorical : Cramer's V, [0,1]
  • Numerical-Categorical : Cramer's V, [0,1] (using a discretized numerical column)
  • Numerical-Numerical : Spearman's ρ, [-1,1]
The number of bins used in the discretization for the Numerical-Categorical column pair can be changed using config.correlations["auto"].n_bins. The number of bins affects the granularity of the association you wish to measure.

This configuration uses the recommended metric for each pair of columns.
2022-12-11T19:34:27.929458image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Spearman's ρ

The Spearman's rank correlation coefficient (ρ) is a measure of monotonic correlation between two variables, and is therefore better in catching nonlinear monotonic correlations than Pearson's r. It's value lies between -1 and +1, -1 indicating total negative monotonic correlation, 0 indicating no monotonic correlation and 1 indicating total positive monotonic correlation.

To calculate ρ for two variables X and Y, one divides the covariance of the rank variables of X and Y by the product of their standard deviations.
2022-12-11T19:34:28.065564image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Pearson's r

The Pearson's correlation coefficient (r) is a measure of linear correlation between two variables. It's value lies between -1 and +1, -1 indicating total negative linear correlation, 0 indicating no linear correlation and 1 indicating total positive linear correlation. Furthermore, r is invariant under separate changes in location and scale of the two variables, implying that for a linear function the angle to the x-axis does not affect r.

To calculate r for two variables X and Y, one divides the covariance of X and Y by the product of their standard deviations.
2022-12-11T19:34:28.200694image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Kendall's τ

Similarly to Spearman's rank correlation coefficient, the Kendall rank correlation coefficient (τ) measures ordinal association between two variables. It's value lies between -1 and +1, -1 indicating total negative correlation, 0 indicating no correlation and 1 indicating total positive correlation.

To calculate τ for two variables X and Y, one determines the number of concordant and discordant pairs of observations. τ is given by the number of concordant pairs minus the discordant pairs divided by the total number of pairs.
2022-12-11T19:34:28.335319image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Phik (φk)

Phik (φk) is a new and practical correlation coefficient that works consistently between categorical, ordinal and interval variables, captures non-linear dependency and reverts to the Pearson correlation coefficient in case of a bivariate normal input distribution. There is extensive documentation available here.

Missing values

2022-12-11T19:34:25.362328image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
A simple visualization of nullity by column.
2022-12-11T19:34:25.515477image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

trade_datefear_and_greed_indexfear_and_greed_slope_3fear_and_greed_slope_8fear_and_greed_slope_21fear_and_greed_ma_3fear_and_greed_ma_8fear_and_greed_ma_21sz_close
02020-12-1170.7819-0.9328000.9773631.30119570.04116771.33745078.2990383347.19
12020-12-1473.8272-0.9328000.9773631.30119570.04116771.33745078.2990383369.12
22020-12-1565.5144-0.9328000.9773631.30119570.04116771.33745078.2990383367.23
32020-12-1667.9835-0.9328000.9773631.30119569.10836771.33745078.2990383366.98
42020-12-1773.2510-0.1920670.9773631.30119568.91630071.33745078.2990383404.87
52020-12-1873.66262.7160670.9773631.30119571.63236771.33745078.2990383394.90
62020-12-2181.48154.4993330.9773631.30119576.13170071.33745078.2990383420.57
72020-12-2264.1975-3.0178330.9773631.30119573.11386771.33745078.2990383356.78
82020-12-2378.60081.6460670.9773631.30119574.75993372.31481378.2990383382.32
92020-12-2471.6049-3.292200-0.2777881.30119571.46773372.03702578.2990383363.11
trade_datefear_and_greed_indexfear_and_greed_slope_3fear_and_greed_slope_8fear_and_greed_slope_21fear_and_greed_ma_3fear_and_greed_ma_8fear_and_greed_ma_21sz_close
4752022-11-2885.02060.987667-0.0514383.91926784.82853386.60493756.5197003078.55
4762022-11-2992.09882.6337670.3806624.09171487.46230086.98560060.6114143149.75
4772022-11-3091.11111.9478670.2983503.83695789.41016787.28395064.4483713151.34
4782022-12-0192.34572.441700-0.0308633.53909591.85186787.25308867.9874673165.47
4792022-12-0289.7942-0.7682000.4835383.40191991.08366787.73662571.3893863156.14
4802022-12-0594.65021.1797001.5740753.07661992.26336789.31070074.4660053211.81
4812022-12-0689.9588-0.7956330.7201632.59454891.46773390.03086277.0605523212.53
4822022-12-0791.35800.5212670.7613122.71212991.98900090.79217579.7726813199.62
4832022-12-0889.7119-1.6461000.5864122.82970590.34290091.37858782.6023863197.35
4842022-12-0982.9630-2.331933-1.1419752.65334388.01096790.23661385.2557293206.95